This open access book presents the assessment framework for IEA’s International Computer an Information Literacy Study (ICILS) 2018, which is designed to assess how well students are prepared for study, work and life in a digital world. The study measures international differences in students’ computer and information literacy (CIL): their ability to use computers to investigate, create, participate and communicate at home, at school, in the workplace and in the community. Participating countries also have an option for their students to complete an assessment of computational thinking (CT). The ICILS assessment framework articulates the basic structure of the study, providing a description of the field and the constructs to be measured. This book outlines the design and content of the measurement instruments, sets down the rationale for those designs, and describes how measures generated by those instruments relate to the constructs. Hypothesized relations between constructs provide the foundation for some of the analyses that follow. Above all, the framework links ICILS to other similar research, enabling the contents of this assessment framework to combine theory and practice in an explication of both the ‘what’ and the ‘how’ of ICILS.

This Open Access book summarizes the key findings from the second cycle of IEA’s International Computer and Information Literacy Study (ICILS), conducted in 2018. ICILS seeks to establish how well schools around the globe are responding to the need to provide young people with the necessary digital participatory competencies. Effective use of information and communication technologies (ICT) is an imperative for successful participation in an increasingly digital world. ICILS 2018 explores international differences in students’ computer and information literacy (CIL), namely their ability to use computers to investigate, create, and communicate at home, at school, in the workplace, and in the community. Participating countries also had an option to administer an assessment of students’ computational thinking (CT), focused on their ability to recognize aspects of real-world problems appropriate for computational formulation, and to evaluate and develop algorithmic solutions to those problems, so that the solutions could be operationalized with a computer. The data collected by ICILS 2018 show how digital competencies can be assessed using instruments representing authentic contexts for ICT use, and how students’ CIL and CT skills relate to school learning experiences, out-of-school contexts, and student characteristics. Those data also show how learning technologies are used in classrooms around the world. Background questionnaires asked students about their use of ICT, and collected information from teachers, schools, and national education systems about the resourcing and teaching of CIL (and CT) within their countries. The results of ICILS 2018 will enable policymakers and education systems to develop a better understanding of the contexts and outcomes of CIL (and CT) education programs.

This This book is open access under a CC BY 4.0 license.This book offers a comprehensive guide, covering every important aspect of computational thinking education. It provides an in-depth discussion of computational thinking, including the notion of perceiving computational thinking practices as ways of mapping models from the abstraction of data and process structures to natural phenomena. Further, it explores how computational thinking education is implemented in different regions, and how computational thinking is being integrated into subject learning in K-12 education. In closing, it discusses computational thinking from the perspective of STEM education, the use of video games to teach computational thinking, and how computational thinking is helping to transform the quality of the workforce in the textile and apparel industry.

This open access book describes and reviews the development of the quality control mechanisms and methodologies associated with IEA’s extensive program of educational research. A group of renowned international researchers, directly involved in the design and execution of IEA’s international large-scale assessments (ILSAs), describe the operational and quality control procedures that are employed to address the challenges associated with providing high-quality, comparable data. Throughout the now considerable history of IEA’s international large-scale assessments, establishing the quality of the data has been paramount. Research in the complex multinational context in which IEA studies operate imposes significant burdens and challenges in terms of the methodologies and technologies that have been developed to achieve the stated study goals. The demands of the twin imperatives of validity and reliability must be satisfied in the context of multiple and diverse cultures, languages, orthographies, educational structures, educational histories, and traditions. Readers will learn about IEA’s approach to such challenges, and the methods used to ensure that the quality of the data provided to policymakers and researchers can be trusted. An often neglected area of investigation, namely the consequential validity of ILSAs, is also explored, examining issues related to reporting, dissemination, and impact, including discussion of the limits of interpretation. The final chapters address the question of the influence of ILSAs on policy and reform in education, including a case study from Singapore, a country known for its outstanding levels of achievement, but which nevertheless seeks the means of continual improvement, illustrating best practice use of ILSA data.

This report addresses the more contentious aspects of large-scale learning assessments (LSLAs). Drawing on UNESCO's extensive experience in the area from involvement in the direct implementation of assessments and as a knowledge broker and convener of networks this publication presents the Organization's critical take on such initiatives. It aims to balance the debate on LSLAs by reviewing their benefits while raising awareness on their potential risks and pitfalls. The focus of discussions in this publication is on LSLAs conducted in formal and school-based education. It includes an Annex outlining key international studies. [Executive summary, ed]

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data